Blockchain Digital Assets (BDAs) are intangible assets issued based on blockchain, providing a new paradigm for managing digital assets. Smart contracts are programs running on the blockchain and enhance the flexibility of BDA in a programmable way. However, scarcity defects in smart contracts can lead to abnormal changes in the number of BDA and affect their worth. Software invariants are logical assertions that a program fragment needs to remain faithful during execution and work well in defect detection. This paper studies the scarcity defect detection method of smart contract digital assets based on invariant analysis for the first time. First, we point out eight scarcity defects in three categories and describe their examples. Next, we propose two invariants—transfer invariant and swap invariant—that should be maintained in digital assets’ management and transaction process. Then, we use the two invariants as test oracles and propose an oracle-based method to detect scarcity defects in smart contract. Finally, we evaluate the proposed method on a real-world smart contract dataset. The experimental results show that our method can effectively detect scarcity defects in smart contracts and improve the scarcity defect detection capability of existing smart contract testing tools.

Crowdsourcing software testing is a software testing model rising in recent years. Although many black box testing modes have achieved good results in crowdsourcing environment, few people try to migrate white box software testing to crowd-sourcing environment. Firstly, this paper analyzes the problems of white box testing in crowdsourcing environment, that is, the contradiction between the sensitivity of intellectual property rights and the insecurity of crowdsourcing environment. Then we focus on the design of the go language unit crowdsourcing test task package. From the perspective of code security and meeting the test conditions, a go language unit crowdsourcing test task package division method based on static analysis is designed. Finally, the possible attack model in the unit crowd test scenario is assumed, and the risk resistance of our task package division method is analyzed.

ERC20 is the first token standard on Ethereum and is widely used in ICOs, voting, and various asset representations. However, some methods defined in ERC20 imply potential vulnerabilities and Multiple Withdrawal Attack is one of them. Attackers can transfer more tokens than the actual allowance through this vulnerability. The current prevention methods for Multiple Withdrawal Attack include changing the transaction process, modifying the API of ERC20, and modifying the implementation of functions, etc. However, these methods have disadvantages such as poor compatibility, incomplete resolution, and high gas consumption. In this paper, we describe the process of Multiple Withdrawal Attack and analyze the shortcomings of the existing methods, and then propose a solution with lower gas consumption. In our method, a variable is added to record the allowance in the approval function to prevent tokens from being transferred repeatedly. Finally, the effectiveness and the performance of the proposed method is analyzed. The result shows that the method proposed in this paper is safe and has lower gas consumption than the existing methods.

Wavelet-based compression is widely used to reduce image redundancy for efficiently storing and transmitting the data. Thus it is an important part in digital image forensics to trace the wavelet-based image compression history. The wavelet-based compression leaves comb-like quantization artifacts in the DWT (Discrete Wavelet Transform) histogram, which however can be disguised using a proper dithering operation. In this paper, standing on the forensic side, we study the joint histogram of DWT coefficients across different levels, whose pattern is robust across a wide range of natural uncompressed images but can be easily destroyed by the wavelet-based compression or the dithering operation used for anti-forensic purposes. By applying the Hough transform to the joint DWT histogram, we derive a 12-dimensional feature vector and a merged discriminating feature. Experimental results demonstrate the effectiveness of the proposed method for differentiating uncompressed images from (anti-forensic) wavelet-based compressed images.